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Security Audit

jami

github.com/openclaw/skills
AI SkillCommit 13146e6a3d46
58
CAUTION
Scanned about 2 months ago
4
Critical
Immediate action required
1
High
Priority fixes suggested
2
Medium
Best practices review
2
Low
Acknowledged / Tracked

Trust Assessment

jami received a trust score of 58/100, placing it in the Caution category. This skill has some security considerations that users should review before deployment.

SkillShield's automated analysis identified 9 findings: 4 critical, 1 high, 2 medium, and 2 low severity. Key findings include Sensitive environment variable access: $HOME, Command Injection via unsanitized CONTACT_ID, Command Injection via unsanitized MESSAGE.

The analysis covered 4 layers: Manifest Analysis, Static Code Analysis, Dependency Graph, LLM Behavioral Safety. The LLM Behavioral Safety layer scored lowest at 0/100, indicating areas for improvement.

Last analyzed on February 13, 2026 (commit 13146e6a). SkillShield performs automated 4-layer security analysis on AI skills and MCP servers.

Layer Breakdown

Manifest Analysis
100%
Static Code Analysis
93%
Dependency Graph
100%
LLM Behavioral Safety
0%

Behavioral Risk Signals

Filesystem Write
4 findings
Shell Execution
8 findings
Dynamic Code
5 findings
Excessive Permissions
3 findings

Security Findings9

SeverityFindingLayerLocation

Scan History

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